Glacier mapping from satellite multispectral image data is hampered by debris cover on glacier surfaces. Information on the spatial distribution and spatial-temporal dynamics of debris, however, bears various kinds of uncertainties. Debris exhibits the same spectral properties as lateral and terminal moraines and as bedrock outside the glacier margin. Multispectral classification alone is thus not suitable to properly assess its extent. Additional information has to be included, like the low slope angles and curvature characteristics. In this research we propose a random set method for uncertainty modelling of debris-covered glaciers extracted from remote sensed data. Here, we analyse the Fedchenko glacier situated in the Pamir mountains in Central Asia. Clean glacier ice and debris area are represented by random sets. Their statistical mean and median are estimated. The paper combines the advantages of an automated multispectral classification for clean glacier ice and snow with slope information derived from the digital elevation model (DEM). We use an SRTM3 DEM that is resampled to 30m. From a 1999 Landsat ETM+ image the results show that the mean area of clean glacier ice equals 841.87 km2, and 94.39 km2 for debris-covered area. Temporal analysis shows that the mean area of clean ice increased from 1992 to 1999 and is decreasing since 1999, in opposite to the debris covered area. We conclude that this method based on random set theory has the potential to serve as a general framework in uncertainty modelling of debris-covered glaciers and is applicable for mountainous glaciers.